Telecommunication networks for mobile devices generally allow mobile devices to move geographically by “handing off” localized communication links among transmission towers and associated base stations. For example, such networks allow Internet Protocol-enabled devices such as wireless Personal Digital Assistants (PDAs) and mobile terminals and computers to move about geographically dispersed areas while maintaining a connection to the Internet.
As is well known, mobile terminals can be served by one or more access routers (ARs) that serve terminals within a particular area. Such access routers allow the mobile terminals to access one or more networks, such as the Internet, using mobile IP protocols or other protocols. Mobile terminals may communicate using one of various access technologies, such as GPRS, Bluetooth, WLAN, or others.
Mobile IP enables a mobile node (MN) to execute IP-level handovers between access routers (ARs) that act as points of attachment to the IP network. Access point (AP) or a base station is a Layer2 device that is connected to one or more access routers (ARs) and offers a wireless connection to the mobile node. Access point may be also implemented in the same entity as access router (AR). However, the handover latency and packet loss incurred by standard Mobile IP are quite high. It is desirable to provide seamless handovers (low latency and low packet loss) between access routers (ARs). Many seamless handover solutions however make an assumption that the mobile node MN and/or the current access router (AR) have a priori knowledge of the target of the handover (i.e., the next access router or target access router). In order to provide this information to these seamless handover solutions, a methodology is desired to discover geographically adjacent routers and to collect their capabilities.
Seamless handover solutions may be vulnerable toward Denial of Service (DoS) attacks. An example of this is that a malicious MN may send false reports to the new AR thereby filling up the new AR's cache with false information. This may lead to denial of service with respect to future requests. Another example is that a malicious MN may send a wrong request that may then be stored in the cache after being resolved. Similar to the first approach, the cache may get filled up by wrong entries.